to ascertain the signal significance for finding a signal of known cross section \(\sigma _{S}\)  and efficiency \(\epsilon _{S}\)  over that of a known background, \(\sigma _{B}\)  and \(\epsilon _{B}\) , for a given integrated luminosity, \(\mathcal {L}\) . Figure REF  shows the mass reconstruction for a Higgs boson mass of 125 GeV, consistent with the CMS and ATLAS observations [1]}[2]}, also shown with the DY background peaking at the Z boson mass. The Higgs boson signal and the DY background have the same number of events and are not normalized according to their cross section. The signal to background significance in the range of 115–135 GeV for SVfit is \(11.2 \pm 0.1\)  and for the neural network \(16.5 \pm 0.2\) , where the cross section taken for the DY background is \(\sigma _{B} = 1418 \pm 1\:\text{pb}\)  and the cross section assumed for the production of a 125 GeV mass Higgs boson is \(\sigma _{S}=0.7002 \pm 0.0006\:\text{pb}\)  for a center-of-mass energy of 13 TeV, both as obtained with MadGraph [3]}. The integrated luminosity is set to a value of \(\mathcal {L}=100\:\text{fb}^{-1}\) , similar to the data sets collected by the ATLAS and CMS experiments until the end of 2017. The efficiency \(\epsilon \)  is the number of events in the signal range divided by the total number of events passing the requirements in section . The DY background events are slightly biased towards higher masses since the neural network is only trained for masses of 80 GeV and above. Using a larger mass range of the training sample would lead to a smaller deviation for the DY background and therefore to an even larger signal to background significance.
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